Next: Maximum Cardinality Matchings in Up: Graph Algorithms Previous: Maximum Cardinality Matchings in Contents Index

Bipartite Weighted Matchings and Assignments ( mwb_matching )

We give functions

to compute maximum and minimum weighted matchings in bipartite graph,
to check the optimality of matchings, and
to scale edge weights, so as to avoid round-off errors in computations with the number type double.

All functions for computing maximum or minimum weighted matchings provide a proof of optimality in the form of a potential function pot; see the chapter on bipartite weighted matchings of the LEDA book for a discussion of potential functions.

The functions in this section are template functions. The template parameter NT can be instantiated with any number type. In order to use the template version of the function the appropriate .h-file must be included.

#include <LEDA/graph/templates/mwb_matching.h>

There are pre-instantiations for the number types int and double.The pre-instantiated versions have the same function names except for the suffix _T. In order to use them either

#include <LEDA/graph/mwb_matching.h>

#include <LEDA/graph/graph_alg.h>

has to be included (the latter file includes the former). The connection between template functions and pre-instantiated functions is discussed in detail in the section ``Templates for Network Algorithms'' of the LEDA book. The function names of the pre-instantiated versions and the template versions only differ by an additional suffix _T in the names of the latter ones.

Special care should be taken when using the template functions with a number type NT that can incur rounding error, e.g., the type double. The section ``Algorithms on Weighted Graphs and Arithmetic Demand'' of the LEDA book contains a general discussion of this issue. The template functions are only guaranteed to perform correctly if all arithmetic performed is without rounding error. This is the case if all numerical values in the input are integers (albeit stored as a number of type NT) and if none of the intermediate results exceeds the maximal integer representable by the number type ( 2⁵³ - 1 in the case of doubles). All intermediate results are sums and differences of input values, in particular, the algorithms do not use divisions and multiplications.

The algorithms have the following arithmetic demands. Let C be the maximal absolute value of any edge cost. If all weights are integral then all intermediate values are bounded by 3C in the case of maximum weight matchings and by 4nC in the case of the other matching algorithms. Let f = 3 in the former case and let f = 4n in the latter case.

The pre-instantiations for number type double compute the optimal matching for a modified weight function c1, where for every edge e

$\mathit{c1}[e]= \mathit{sign}(c[e]) \lfloor \Vert \mathit{c}[\mathit{e}] \Vert * S \rfloor / S$
and S is the largest power of two such that S < 2⁵³/(f*C).

The weight of the optimal matching for the modified weight function and the weight of the optimal matching for the original weight function differ by at most n*f*C*2^-52.

template <class NT>

list<edge> MAX_WEIGHT_BIPARTITE_MATCHING_T(graph& G, const edge_array<NT>& c, node_array<NT>& pot)

computes a matching of maximal cost and a potential function pot that is tight with respect to M. The running time of the algorithm is O(n*(m + n log n)). The argument pot is optional.
Precondition G must be bipartite.

template <class NT>

list<edge> MAX_WEIGHT_BIPARTITE_MATCHING_T(graph& G, const list<node>& A, const list<node>& B, const edge_array<NT>& c, node_array<NT>& pot)

As above. It is assumed that the partition (A, B) witnesses that G is bipartite and that all edges of G are directed from A to B. If A and B have different sizes then is is advisable that A is the smaller set; in general, this leads to smaller running time. The argument pot is optional.

template <class NT>

bool CHECK_MWBM_T(const graph& G, const edge_array<NT>& c, const list<edge>& M, const node_array<NT>& pot)

checks that pot is a tight feasible potential function with respect to M and that M is a matching. Tightness of pot implies that M is a maximum weighted matching.

template <class NT>

list<edge> MAX_WEIGHT_ASSIGNMENT_T(graph& G, const edge_array<NT>& c, node_array<NT>& pot)

computes a perfect matching of maximal cost and a potential function pot that is tight with respect to M. The running time of the algorithm is O(n*(m + n log n)). If G contains no perfect matching the empty set of edges is returned. The argument pot is optional.
Precondition G must be bipartite.

template <class NT>

list<edge> MAX_WEIGHT_ASSIGNMENT_T(graph& G, const list<node>& A, const list<node>& B, const edge_array<NT>& c, node_array<NT>& pot)

As above. It is assumed that the partition (A, B) witnesses that G is bipartite and that all edges of G are directed from A to B. The argument pot is optional.

template <class NT>

bool CHECK_MAX_WEIGHT_ASSIGNMENT_T(const graph& G, const edge_array<NT>& c, const list<edge>& M, const node_array<NT>& pot)

checks that pot is a tight feasible potential function with respect to M and that M is a perfect matching. Tightness of pot implies that M is a maximum cost assignment.

template <class NT>

list<edge> MIN_WEIGHT_ASSIGNMENT_T(graph& G, const edge_array<NT>& c, node_array<NT>& pot)

computes a perfect matching of minimal cost and a potential function pot that is tight with respect to M. The running time of the algorithm is O(n*(m + n log n)). If G contains no perfect matching the empty set of edges is returned. The argument pot is optional.
Precondition G must be bipartite.

template <class NT>

list<edge> MIN_WEIGHT_ASSIGNMENT_T(graph& G, const list<node>& A, const list<node>& B, const edge_array<NT>& c, node_array<NT>& pot)

As above. It is assumed that the partition (A, B) witnesses that G is bipartite and that all edges of G are directed from A to B. The argument pot is optional.

template <class NT>

bool CHECK_MIN_WEIGHT_ASSIGNMENT_T(const graph& G, const edge_array<NT>& c, const list<edge>& M, const node_array<NT>& pot)

checks that pot is a tight feasible potential function with respect to M and that M is a perfect matching. Tightness of pot implies that M is a minimum cost assignment.

template <class NT>

list<edge> MWMCB_MATCHING_T(graph& G, const list<node>& A, const list<node>& B, const edge_array<NT>& c, node_array<NT>& pot)

Returns a maximum weight matching among the matchings of maximum cardinality. The potential function pot is tight with respect to a modified cost function which increases the cost of every edge by L = 1 + 2kC where C is the maximum absolute value of any weight and k = min(| A|,| B|). It is assumed that the partition (A, B) witnesses that G is bipartite and that all edges of G are directed from A to B. If A and B have different sizes, it is advisable that A is the smaller set; in general, this leads to smaller running time. The argument pot is optional.

bool MWBM_SCALE_WEIGHTS(const graph& G, edge_array<double>& c)

replaces c[e] by c1[e] for every edge e, where c1[e] was defined above and f = 3. This scaling function is appropriate for the maximum weight matching algorithm. The function returns false if the scaling changed some weight, and returns true otherwise.

bool MWA_SCALE_WEIGHTS(const graph& G, edge_array<double>& c)

replaces c[e] by c1[e] for every edge e, where c1[e] was defined above and f = 4n. This scaling function should be used for the algorithms that compute minimum of maximum weight assignments or maximum weighted matchings of maximum cardinality. The function returns false if the scaling changed some weight, and returns true otherwise.

Next: Maximum Cardinality Matchings in Up: Graph Algorithms Previous: Maximum Cardinality Matchings in Contents Index

template <class NT>
list<edge>	MAX_WEIGHT_BIPARTITE_MATCHING_T(graph& G, const edge_array<NT>& c, node_array<NT>& pot)
		computes a matching of maximal cost and a potential function pot that is tight with respect to M. The running time of the algorithm is O(n(m + n log* n)). The argument pot is optional. Precondition G must be bipartite.
template <class NT>
list<edge>	MAX_WEIGHT_BIPARTITE_MATCHING_T(graph& G, const list<node>& A, const list<node>& B, const edge_array<NT>& c, node_array<NT>& pot)
		As above. It is assumed that the partition (A, B) witnesses that G is bipartite and that all edges of G are directed from A to B. If A and B have different sizes then is is advisable that A is the smaller set; in general, this leads to smaller running time. The argument pot is optional.
template <class NT>
bool	CHECK_MWBM_T(const graph& G, const edge_array<NT>& c, const list<edge>& M, const node_array<NT>& pot)
		checks that pot is a tight feasible potential function with respect to M and that M is a matching. Tightness of pot implies that M is a maximum weighted matching.
template <class NT>
list<edge>	MAX_WEIGHT_ASSIGNMENT_T(graph& G, const edge_array<NT>& c, node_array<NT>& pot)
		computes a perfect matching of maximal cost and a potential function pot that is tight with respect to M. The running time of the algorithm is O(n(m + n log* n)). If G contains no perfect matching the empty set of edges is returned. The argument pot is optional. Precondition G must be bipartite.
template <class NT>
list<edge>	MAX_WEIGHT_ASSIGNMENT_T(graph& G, const list<node>& A, const list<node>& B, const edge_array<NT>& c, node_array<NT>& pot)
		As above. It is assumed that the partition (A, B) witnesses that G is bipartite and that all edges of G are directed from A to B. The argument pot is optional.
template <class NT>
bool	CHECK_MAX_WEIGHT_ASSIGNMENT_T(const graph& G, const edge_array<NT>& c, const list<edge>& M, const node_array<NT>& pot)
		checks that pot is a tight feasible potential function with respect to M and that M is a perfect matching. Tightness of pot implies that M is a maximum cost assignment.
template <class NT>
list<edge>	MIN_WEIGHT_ASSIGNMENT_T(graph& G, const edge_array<NT>& c, node_array<NT>& pot)
		computes a perfect matching of minimal cost and a potential function pot that is tight with respect to M. The running time of the algorithm is O(n(m + n log* n)). If G contains no perfect matching the empty set of edges is returned. The argument pot is optional. Precondition G must be bipartite.
template <class NT>
list<edge>	MIN_WEIGHT_ASSIGNMENT_T(graph& G, const list<node>& A, const list<node>& B, const edge_array<NT>& c, node_array<NT>& pot)
		As above. It is assumed that the partition (A, B) witnesses that G is bipartite and that all edges of G are directed from A to B. The argument pot is optional.
template <class NT>
bool	CHECK_MIN_WEIGHT_ASSIGNMENT_T(const graph& G, const edge_array<NT>& c, const list<edge>& M, const node_array<NT>& pot)
		checks that pot is a tight feasible potential function with respect to M and that M is a perfect matching. Tightness of pot implies that M is a minimum cost assignment.
template <class NT>
list<edge>	MWMCB_MATCHING_T(graph& G, const list<node>& A, const list<node>& B, const edge_array<NT>& c, node_array<NT>& pot)
		Returns a maximum weight matching among the matchings of maximum cardinality. The potential function pot is tight with respect to a modified cost function which increases the cost of every edge by L = 1 + 2kC where C is the maximum absolute value of any weight and k = min(\| A\|,\| B\|). It is assumed that the partition (A, B) witnesses that G is bipartite and that all edges of G are directed from A to B. If A and B have different sizes, it is advisable that A is the smaller set; in general, this leads to smaller running time. The argument pot is optional.
bool	MWBM_SCALE_WEIGHTS(const graph& G, edge_array<double>& c)
		replaces c[e] by c1[e] for every edge e, where c1[e] was defined above and f = 3. This scaling function is appropriate for the maximum weight matching algorithm. The function returns false if the scaling changed some weight, and returns true otherwise.
bool	MWA_SCALE_WEIGHTS(const graph& G, edge_array<double>& c)
		replaces c[e] by c1[e] for every edge e, where c1[e] was defined above and f = 4n. This scaling function should be used for the algorithms that compute minimum of maximum weight assignments or maximum weighted matchings of maximum cardinality. The function returns false if the scaling changed some weight, and returns true otherwise.